Customer Churn Problem

Problem Background

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. Customer churn refers to the situation when a customer ends their relationship with a company, and it’s a costly problem. Customers are the fuel that powers a business. Loss of customers impacts sales. Further, it’s much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn.

The dataset used for this Keras tutorial is IBM Watson Telco Dataset. According to IBM, the business challenge is:

“A telecommunications company [Telco] is concerned about the number of customers leaving their landline business for cable competitors. They need to understand who is leaving. Imagine that you’re an analyst at this company and you have to find out who is leaving and why.”

We are going to use Keras libraryto develop a sophisticated and highly accurate deep learning model in Python. We walk you through the preprocessing steps, investing time into how to format the data for Keras.

Finally we show you how to get black box (NN) insights using the recently developed lime package.

Test Loss and Test Accuracy

AUC

Precision and Recall

F1 Score

Inspect Performance of specific samples

Confusion Table